NVIDIA Aerial™ Framework

Overview

NVIDIA Aerial™ Framework has been designed from the ground up to meet the needs of 3GPP Radio Access Networks — signal processing workloads with microsecond latency requirements. It is a single platform that unites research, testbeds, and production deployments to solve development challenges for real-time applications.

Use cases: Signal processing applications with strict latency requirements
\nAudience: RAN system engineers, signal processing specialists, AI researchers
\nBuilt with: DOCA, DPDK, TensorRT, Python, JAX, PyTorch, C++, CUDA, and more

The Aerial Framework combines two components:

Developer tools: Tools to convert Python/JAX/PyTorch and C++/CUDA into pipelines of GPU-native code
Runtime engine: Coordinates the execution of GPU pipelines with network interfaces

In addition to this container, source code is available on GitHub

NVIDIA Aerial™ Framework is a part of NVIDIA AI Aerial™,\na portfolio of accelerated computing platforms, software and tools to\nbuild, train, simulate, and deploy AI-native wireless networks.

Quickstart

Install the Docker container, then explore and build from source:

# 1) Configure (release preset)\ncmake --preset clang-release\n\n# 2) Build\ncmake --build out/build/clang-release\n\n# 3) Install Example Python Package - 5G RAN\ncd ran/py && uv sync\n

Documentation & Tutorials

Documentation is available at: docs.nvidia.com/aerial/framework

Get started with step-by-step Tutorials.

Support

File issues on GitHub for bugs and feature requests
Join discussions on GitHub for questions and community support

Acknowledgements

See Acknowledgements

Citation

If you use NVIDIA Aerial™ Framework in your research, please cite:

@software{nvidia_aerial_framework,\n  title = {NVIDIA Aerial™ Framework},\n  author = {NVIDIA Corporation},\n  year = {2025},\n  url = {https://github.com/NVIDIA/aerial-framework}\n}\n

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\n**Audience: RAN** system engineers, signal processing specialists, AI researchers
\n**Built with**: DOCA, DPDK, TensorRT, Python, JAX, PyTorch, C++, CUDA, and more\n\nThe Aerial Framework combines two components:\n- **Developer tools**: Tools to convert Python/JAX/PyTorch and C++/CUDA into pipelines of GPU-native code\n- **Runtime engine**: Coordinates the execution of GPU pipelines with network interfaces \n\nIn addition to this container, source code is available on [GitHub](https://github.com/NVIDIA/aerial-framework)\n\nNVIDIA Aerial™ Framework is a part of [NVIDIA AI Aerial™](https://developer.nvidia.com/industries/telecommunications/ai-aerial),\na portfolio of accelerated computing platforms, software and tools to\nbuild, train, simulate, and deploy AI-native wireless networks.\n\n## Quickstart\n\n[**Install**](https://docs.nvidia.com/aerial/framework/latest/installation/index.html) the Docker container, then explore and build from source:\n\n```bash\n# 1) Configure (release preset)\ncmake --preset clang-release\n\n# 2) Build\ncmake --build out/build/clang-release\n\n# 3) Install Example Python Package - 5G RAN\ncd ran/py && uv sync\n```\n\n## Documentation & Tutorials\n\nDocumentation is available at: [**docs.nvidia.com/aerial/framework**](https://docs.nvidia.com/aerial/framework/latest/overview/index.html)\n\n\nGet started with step-by-step [**Tutorials**](https://docs.nvidia.com/aerial/framework/latest/tutorials/index.html).\n\n## Support\n\n- File issues on GitHub for bugs and feature requests\n- Join discussions on GitHub for questions and community support\n\n## Acknowledgements\n\nSee [Acknowledgements](https://docs.nvidia.com/aerial/framework/latest/acknowledgements.html) \n\n## Citation\n\nIf you use NVIDIA Aerial™ Framework in your research, please cite:\n\n```bibtex\n@software{nvidia_aerial_framework,\n title = {NVIDIA Aerial™ Framework},\n author = {NVIDIA Corporation},\n year = {2025},\n url = {https://github.com/NVIDIA/aerial-framework}\n}\n```",["D",1765347684898],[214],"/api/image/nvidia/aerial/containers/aerial-framework-base","hasSignedTag",[],[260,261,262,263,264,265,266],[],[256],[],{"_93":257,"_125":257,"_126":258,"_128":259},"signed images",{"_130":259,"_125":257},"system",{"_93":286,"_125":287,"_126":288,"_128":129},{"_93":282,"_125":283,"_126":284,"_128":129},{"_93":279,"_125":280,"_126":281,"_128":129},{"_93":276,"_125":277,"_126":278,"_128":129},{"_93":272,"_125":273,"_126":274,"_128":129},{"_93":124,"_125":124,"_126":271,"_128":129},{"_93":267,"_125":268,"_126":269,"_128":129},"Telecommunications","indus_telecommunications",{"_130":270,"_125":268},"industry",{"_130":129,"_125":124},"ML","soln_ml",{"_130":275,"_125":273},"technology","Developer Tools","soln_developer_tools",{"_130":275,"_125":277},"AI","soln_ai",{"_130":275,"_125":280},"Aerial","pltfm_aerial",{"_130":285,"_125":283},"platform","Academia / Higher Education","indus_academia_higher_education",{"_130":270,"_125":287},{"_156":1459,"_158":1460},"ngcApps",[1412],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1393":1394,"_1395":1396,"_1397":1398,"_1399":1400,"_1401":300,"_187":324,"_1402":1403,"_1404":51,"_1405":136,"_1406":136,"_1407":1408,"_1409":136,"_1410":1411},"amplitude",{"_1390":1391,"_996":-7,"_1392":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1384":1385,"_1386":1387,"_1388":1389},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1271":404,"_1272":1273,"_1274":1275,"_1276":1277},"announcements",[],"consentState",{"_361":1261,"_1262":136,"_1263":26,"_1264":26,"_1265":1266},"consentConfig",{"_343":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354,"_355":356,"_357":358,"_359":360,"_361":362},"showContactAdmin",1783986383497,[],[],[338,339,340,341,342],{"_40":62,"_42":67,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":62,"_42":65,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":49,"_50":51},{"_40":41,"_42":43,"_44":64,"_46":-5,"_48":-5,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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